def main(argv):
if argv[1] == 'train_process':
get_data()
data_preparation = DataPreparation()
data_preparation.generate_data_for_model()
train_model = Train()
train_model.compute_locations_models()
prediction = Prediction()
prediction.get_models()
create_dashboard(prediction)
def __init__(self, model_deciding=None):
get_time()
self.data_path = data_path if model_deciding == 'all' else features_data_path
self.data = get_data(path=self.data_path, is_for_model=False)
self.columns = list(self.data.columns)
self.features = decide_feature_name(feature_path)
self.model_deciding = model_deciding
def plot_subject(subject, session, options):
"""Load, Process and plot data for one subject/session pair.
Possible options: ('ecg','gsr','blocks','trials','plot_beats','show','save')
"""
# transform all keys to lower case
options = {key.lower():value for key, value in options.items()}
# add missing keys to options and transform to namedtuple
option_keys =('do_ecg','do_gsr','do_blocks','do_trials')
default_opts = dict.fromkeys(option_keys, False)
default_opts.update(options)
if 'figsize' not in default_opts:
default_opts['figsize'] = (15,10)
options = namedtuple('Options', default_opts.keys())(**default_opts)
physio_data, trials, time_range = da.get_data(subject, session)
results = dpp.process_data(physio_data, trials, subject, session, options)
names,figs = plot_results(results, options)
start_time = datetime.fromtimestamp(time_range[0]).strftime('%Y-%m-%d %H:%M:%S')
end_time = datetime.fromtimestamp(time_range[1]).strftime('%Y-%m-%d %H:%M:%S')
print 'start_time:', start_time, ' end_time:', end_time
if options.save:
for name,fig in zip(names,figs):
filename = subject + '_' + session + '_' + name
save_plot(filename, fig)
if options.show:
plt.show()
def __init__(self,
hyper_parameters=None,
model_deciding=None,
last_day_predictor=None,
params=None):
get_time()
self.data = get_data(main_data_path + params['args']['data'],
True) # data that created at feature engineering
self.features = list(
decide_feature_name(main_data_path +
params['args']['feature_set']).keys())
self.params = hyper_parameters # get hyper parameters for model: hyper_parameters.json
self.model_params = params
self.train, self.test = None, None
self.X = None
self.optimum_cluster_centroids = None
self.centroids = None
self.po_data = None # Possible_outlier_transactions data
self.model_dbscan = None
self.m_s, self.eps = [], []
self.o_min_sample = None
self.o_epsilon = None
self.o_devision = None
self.last_day_predictor = last_day_predictor # splitting data indicator
self.uids = None
def main(args):
logger.get_time()
if is_local_run:
args = sample_args
sys.stdout = logger.Logger()
print("*"*3, " args :", args)
if len(args) != 0:
if (args[1]) == 'feature_engineering':
"""
run from terminal: python main.py feature_engineering all
all: create all features which are at features.json
Ex: 'python main.py feature_engineering c_m_ratios' create only 'c_m_ratios' adds to features set.
"""
create_feature = CreateFeatures(model_deciding=args[2])
create_feature.compute_features()
if (args[1]) == 'train_process':
# TODO: description must be updated
"""
run from terminal: python main.py train_process 0
0/1: 0; test data splits from date
1: test data is last day of each customer
Models: isolation forest and AutoEncoder for Multivariate and Univariate Models
"""
train = trainModel(args=args)
train.process()
if sys.argv[1] == 'prediction':
# TODO: description must be updated
"""
run from terminal: python main.py prediction 0
0/1: 0; test data splits from date
1: test data is last day of each customer
It creates prediction values for each transaction is added to raw data set
"""
prediction = trainModel(args=args, is_prediction=True)
prediction.process()
if args[1] == 'dashboard':
# TODO: description must be updated
"""
run from terminal: python main.py dashboard 0 # 10.20.10.196:3030
run from terminal: python main.py dashboard 0 uni # 10.20.10.196:3031
0/1: 0; test data splits from date
1: test data is last day of each customer
uni: creates only for univariate models. I order to run for multivariate dashboard assign null
Dashboard for Multi - Uni Models is created
"""
# TODO: get prediction data from predicted .csv file
model = ModelTrainIsolationForest(last_day_predictor=int(args[2]))
model.train_test_split()
create_dahboard(model.train, get_data(pred_data_path, True))
logger.get_time()
def __init__(self,
hyper_parameters=None,
last_day_predictor=None,
params=None):
get_time()
self.data = get_data(features_data_path,
True) # data that created at feature engineering
# TODO: get specific feature from specific model.
self.features = list(decide_feature_name(feature_path).keys())
self.params = hyper_parameters # get hyper parameters for model: hyper_parameters.json
self.model_params = params
self.train, self.test = None, None
self.X = None
self.model_e_iso_f = None
self.last_day_predictor = last_day_predictor # splitting data indicator
def __init__(self,
hyper_parameters=None,
last_day_predictor=None,
params=None):
get_time()
self.data = get_data(features_data_path, True)
self.features = list(decide_feature_name(feature_path).keys())
self.params = hyper_parameters
self.last_day_predictor = last_day_predictor
self.model_params = params
self.train, self.test = None, None
self.X, self.y_pred, self.y = None, None, None
self.input, self.fr_output = None, None
self.model_ae, self.model_ae_l, self.model_u = None, None, None
self.gpu_devices = [
d for d in device_lib.list_local_devices()
if d.device_type == "GPU"
] if run_gpu else []
def plot_subject(subject, session, options):
"""Load, Process and plot data for one subject/session pair.
Possible options: ('ecg','gsr','blocks','trials','plot_beats','show','save')
"""
# transform all keys to lower case
options = {key.lower(): value for key, value in options.items()}
# add missing keys to options and transform to namedtuple
option_keys = ('do_ecg', 'do_gsr', 'do_blocks', 'do_trials')
default_opts = dict.fromkeys(option_keys, False)
default_opts.update(options)
if 'figsize' not in default_opts:
default_opts['figsize'] = (15, 10)
options = namedtuple('Options', default_opts.keys())(**default_opts)
physio_data, trials, time_range = da.get_data(subject, session)
results = dpp.process_data(physio_data, trials, subject, session, options)
names, figs = plot_results(results, options)
start_time = datetime.fromtimestamp(
time_range[0]).strftime('%Y-%m-%d %H:%M:%S')
end_time = datetime.fromtimestamp(
time_range[1]).strftime('%Y-%m-%d %H:%M:%S')
print 'start_time:', start_time, ' end_time:', end_time
if options.save:
for name, fig in zip(names, figs):
filename = subject + '_' + session + '_' + name
save_plot(filename, fig)
if options.show:
plt.show()
"""
run from terminal: python main.py train_process 0
0/1: 0; test data splits from date
1: test data is last day of each customer
Models: isolation forest & AutoEncoder & DBScan
"""
train = trainModel(args=sys.argv)
train.process()
if sys.argv[1] == 'prediction':
"""
run from terminal: python main.py prediction 0
0/1: 0; test data splits from date
1: test data is last day of each customer
It creates prediction values for each transaction is added to raw data set
"""
prediction = trainModel(args=sys.argv, is_prediction=True)
prediction.process()
if sys.argv[1] == 'dashboard':
"""
run from terminal: python main.py dashboard 0
0/1: 0; test data splits from date
1: test data is last day of each customer
"""
model = ModelTrainIsolationForest(
last_day_predictor=int(sys.argv[2]))
model.train_test_split()
create_dahboard(model.train, get_data(pred_data_path, True))
logger.get_time()
def save_raw_table():
#subjects = [403, 416, 421, 424, 430, 433, 434, 437, 419, 420, 425, 426, 428, 429, 432, 436]
#subjects = [314, 319,321,323,325,326,327,328,332,333]
subjects = [312,314,315,317,320,322,
329,330,332,403,416,419,
420,421,424,425,426,428,
430,432,433,436,437]
path = da.config['PATH']['physio_path']
version_major = 5
version_minor = 0
path = os.path.join(path, 'gsr_to_gamedata_table_v' + str(version_major) + '.' + str(version_minor) + '.csv')
options = {
'do_gsr' : True,
'do_trials' : True,
'only_success' : False,
'silent' : True,
'overwrite' : True,
}
# transform all keys to lower case
options = {key.lower():value for key, value in options.items()}
# add missing keys to options and transform to namedtuple
option_keys =('do_ecg','do_gsr','do_blocks','do_trials','only_success', 'silent','overwrite')
default_opts = dict.fromkeys(option_keys, False)
default_opts.update(options)
# convert to named tuple for easier access
options = namedtuple('Options', default_opts.keys())(**default_opts)
# if file already exists cancel everything
if not options.overwrite and os.path.exists(path):
print path, 'file already exists'
return
# write column names to csv file
head = 'subject,session,physio_time,raw_gsr,condition,trial_id,success\n'
with open(path,'w') as f:
f.write(head)
# bring subject and session in a form to easy iterate over
sessions = [1,2]*len(subjects)
subjects = itertools.chain(*zip(subjects,subjects))
for subject, session in zip(subjects, sessions) :
print 'Processing subject %s session %s' % (subject, session)
subject = str(subject)
session = str(session)
# skip
try:
physio_data, trials, time_range = da.get_data(subject, session, options.only_success, options.silent)
except da.DataAccessError as e:
print('Skip subject %s session %s: %s' % (subject, session, e))
continue
time_scale = np.array(physio_data['time'])
#print len(time_range), len(time_scale)
if len(time_scale) == 0 :
raise Exception('not physio data')
results = dpp.process_data(physio_data, trials, subject, session, options)
raw_gsr = physio_data['gsr']
cond_for_physio = results.conditions_for_physio
trial_for_physio = results.trial_ids_for_physio
success_for_physio = results.success_for_physio
lines = [ ','.join([subject, session, str(t), str(gsr), str(cond), str(tid), str(sfp)+ '\n']) for t,gsr,cond,tid,sfp in \
zip(time_scale, raw_gsr, cond_for_physio, trial_for_physio, success_for_physio) ]
with open(path,'a') as f :
f.writelines(lines)
def save_mean_table() :
subjects = [403, 416, 421, 424, 430, 433, 434, 437,
419, 420, 425, 426, 428, 429, 432, 436]
path = da.config['PATH']['physio_path']
path = os.path.join(path, 'gsr_results_table.csv')
options = {
'do_gsr' : True,
'do_trials' : True,
'only_success' : False,
'silent' : True,
}
# transform all keys to lower case
options = {key.lower():value for key, value in options.items()}
# add missing keys to options and transform to namedtuple
option_keys =('do_ecg','do_gsr','do_blocks','do_trials','silent')
default_opts = dict.fromkeys(option_keys, False)
default_opts.update(options)
# convert to named tuple for easier access
options = namedtuple('Options', default_opts.keys())(**default_opts)
# if file already exists cancel everything
if os.path.exists(path) :
print path, 'file already exists'
return
# write column names to csv file
head = 'subject,session,trial_id,condition,mean_gsr\n'
with open(path,'w') as f :
f.write(head)
# bring subject and session in a form to easy iterate over
sessions = [1,2]*len(subjects)
subjects = itertools.chain(*zip(subjects,subjects))
for subject, session in zip(subjects, sessions) :
print 'Processing subject %s session %s' % (subject, session)
subject = str(subject)
session = str(session)
# try to load data
try:
physio_data, trials, time_range = da.get_data(subject, session, options.only_success)
except da.DataAccessError as e:
print('Skip subject %s session %s: %s' % (subject, session, e))
continue
time_scale = np.array(physio_data['time'])
#print len(time_range), len(time_scale)
if len(time_scale) == 0 :
raise Exception('not physio data')
results = dpp.process_data(physio_data, trials, subject, session, options)
condition = trials[2]
trial_id = trials[3]
trails = trials[:-1]
gsr_mean = results.mean_gsr_for_trials
trials = zip(*trials)
lines = [ ','.join([subject,session,str(tid),cond,str(gsr) + '\n']) for tid,cond,gsr in \
zip(trial_id, condition, gsr_mean) ]
with open(path,'a') as f :
f.writelines(lines)
def save_raw_table():
#subjects = [403, 416, 421, 424, 430, 433, 434, 437, 419, 420, 425, 426, 428, 429, 432, 436]
#subjects = [314, 319,321,323,325,326,327,328,332,333]
subjects = [
312, 314, 315, 317, 320, 322, 329, 330, 332, 403, 416, 419, 420, 421,
424, 425, 426, 428, 430, 432, 433, 436, 437
]
path = da.config['PATH']['physio_path']
version_major = 5
version_minor = 0
path = os.path.join(
path, 'gsr_to_gamedata_table_v' + str(version_major) + '.' +
str(version_minor) + '.csv')
options = {
'do_gsr': True,
'do_trials': True,
'only_success': False,
'silent': True,
'overwrite': True,
}
# transform all keys to lower case
options = {key.lower(): value for key, value in options.items()}
# add missing keys to options and transform to namedtuple
option_keys = ('do_ecg', 'do_gsr', 'do_blocks', 'do_trials',
'only_success', 'silent', 'overwrite')
default_opts = dict.fromkeys(option_keys, False)
default_opts.update(options)
# convert to named tuple for easier access
options = namedtuple('Options', default_opts.keys())(**default_opts)
# if file already exists cancel everything
if not options.overwrite and os.path.exists(path):
print path, 'file already exists'
return
# write column names to csv file
head = 'subject,session,physio_time,raw_gsr,condition,trial_id,success\n'
with open(path, 'w') as f:
f.write(head)
# bring subject and session in a form to easy iterate over
sessions = [1, 2] * len(subjects)
subjects = itertools.chain(*zip(subjects, subjects))
for subject, session in zip(subjects, sessions):
print 'Processing subject %s session %s' % (subject, session)
subject = str(subject)
session = str(session)
# skip
try:
physio_data, trials, time_range = da.get_data(
subject, session, options.only_success, options.silent)
except da.DataAccessError as e:
print('Skip subject %s session %s: %s' % (subject, session, e))
continue
time_scale = np.array(physio_data['time'])
#print len(time_range), len(time_scale)
if len(time_scale) == 0:
raise Exception('not physio data')
results = dpp.process_data(physio_data, trials, subject, session,
options)
raw_gsr = physio_data['gsr']
cond_for_physio = results.conditions_for_physio
trial_for_physio = results.trial_ids_for_physio
success_for_physio = results.success_for_physio
lines = [ ','.join([subject, session, str(t), str(gsr), str(cond), str(tid), str(sfp)+ '\n']) for t,gsr,cond,tid,sfp in \
zip(time_scale, raw_gsr, cond_for_physio, trial_for_physio, success_for_physio) ]
with open(path, 'a') as f:
f.writelines(lines)
def save_mean_table():
subjects = [
403, 416, 421, 424, 430, 433, 434, 437, 419, 420, 425, 426, 428, 429,
432, 436
]
path = da.config['PATH']['physio_path']
path = os.path.join(path, 'gsr_results_table.csv')
options = {
'do_gsr': True,
'do_trials': True,
'only_success': False,
'silent': True,
}
# transform all keys to lower case
options = {key.lower(): value for key, value in options.items()}
# add missing keys to options and transform to namedtuple
option_keys = ('do_ecg', 'do_gsr', 'do_blocks', 'do_trials', 'silent')
default_opts = dict.fromkeys(option_keys, False)
default_opts.update(options)
# convert to named tuple for easier access
options = namedtuple('Options', default_opts.keys())(**default_opts)
# if file already exists cancel everything
if os.path.exists(path):
print path, 'file already exists'
return
# write column names to csv file
head = 'subject,session,trial_id,condition,mean_gsr\n'
with open(path, 'w') as f:
f.write(head)
# bring subject and session in a form to easy iterate over
sessions = [1, 2] * len(subjects)
subjects = itertools.chain(*zip(subjects, subjects))
for subject, session in zip(subjects, sessions):
print 'Processing subject %s session %s' % (subject, session)
subject = str(subject)
session = str(session)
# try to load data
try:
physio_data, trials, time_range = da.get_data(
subject, session, options.only_success)
except da.DataAccessError as e:
print('Skip subject %s session %s: %s' % (subject, session, e))
continue
time_scale = np.array(physio_data['time'])
#print len(time_range), len(time_scale)
if len(time_scale) == 0:
raise Exception('not physio data')
results = dpp.process_data(physio_data, trials, subject, session,
options)
condition = trials[2]
trial_id = trials[3]
trails = trials[:-1]
gsr_mean = results.mean_gsr_for_trials
trials = zip(*trials)
lines = [ ','.join([subject,session,str(tid),cond,str(gsr) + '\n']) for tid,cond,gsr in \
zip(trial_id, condition, gsr_mean) ]
with open(path, 'a') as f:
f.writelines(lines)
